Locality-Sensitive Hashing
Locality-Sensitive Hashing (LSH) is a technique in computer science and data mining used for approximate nearest neighbor search in high-dimensional spaces. It works by hashing input items so that similar items map to the same 'buckets' with high probability, while dissimilar items are likely to be placed in different buckets. This enables efficient similarity search and clustering in large datasets, such as for image retrieval, document deduplication, or recommendation systems.
Developers should learn LSH when dealing with large-scale similarity search problems where exact methods are computationally infeasible, such as in machine learning, data mining, or database applications. It is particularly useful for tasks like near-duplicate detection in web pages, content-based image retrieval, or building recommendation engines, as it reduces search time from linear to sub-linear complexity while maintaining acceptable accuracy.